WH_fcmeans | R Documentation |
The function implements the fuzzy c-means for a set of histogram-valued data.
WH_fcmeans(x, k, m = 1.6, rep, simplify = FALSE, qua = 10, standardize = FALSE)
x |
A MatH object (a matrix of distributionH). |
k |
An integer, the number of groups. |
m |
A number grater than 0, a fuzziness coefficient (default |
rep |
An integer, maximum number of repetitions of the algorithm (default |
simplify |
A logic value (default is FALSE), if TRUE histograms are recomputed in order to speed-up the algorithm. |
qua |
An integer, if |
standardize |
A logic value (default is FALSE). If TRUE, histogram-valued data are standardized, variable by variable, using the Wassertein based standard deviation. Use if one wants to have variables with std equal to one. |
a list with the results of the fuzzy c-means of the set of Histogram-valued data x
into k
cluster.
solution
A list.Returns the best solution among the rep
etitions, i.e.
the one having the minimum sum of squares deviation.
solution$membership
A matrix. The membership degree of each unit to each cluster.
solution$IDX
A vector. The crisp assignement to a cluster.
solution$cardinality
A vector. The cardinality of each final cluster (after the crisp assignement).
solution$Crit
A number. The criterion (Sum of square deviation from the prototypes) value at the end of the run.
quality
A number. The percentage of Sum of square deviation explained by the model. (The higher the better)
results <- WH_fcmeans(x = BLOOD, k = 2, m = 1.5, rep = 10,
simplify = TRUE, qua = 10, standardize = TRUE)
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